Systems biology approaches offer the promise of quantitative models that shed light on aspects of basic cancer biology. Equally, there is much potential in machine learning approaches that exploit high-throughput data to explore the heterogeneity of cancer and assist in targeting complex therapies to patients. However, while the promise of systems approaches is clear, the challenges posed by noisy and incomplete data, inherent biological variability and complex underlying processes and dynamics remain substantial.
Our research is aimed at developing and exploiting stochastic models and statistical methods that can help to surmount some of these challenges. Networks and graphical models, high-dimensional problems, combinatorial influences and inference for dynamical systems are key methodological themes in much of our work. Papers and code can be found here.
